Green computing is sometimes defined as the study and practice of using computing resources efficiently. Typically, technological systems or computing products that incorporate green computing principles take into account economic viability, social responsibility, and environmental impact. A typical green computing method includes implementing environmentally friendly products, like those with the Energy Star rating, in an efficient system that maximizes energy use.
One of the goals of green computing is to use power generated from sources that are more environmentally friendly than coal-fired power stations. Some companies provide power from low impact sources, like windmills and hydroelectric dams. Other sources include locally-installed photovoltaic panels, which generate electrical energy from the sun, or power produced by nuclear power plants.
Point solutions for energy efficiency are relatively straightforward for data centers to implement, like orienting racks of servers in a data center to exhaust their heat in a uniform direction, reducing overall cooling costs. Difficulties arise where individual personal computer users are tasked with implementing energy and power savings themselves. As is known by those skilled in the art, devices and operating systems allow users to select suggested configurations for reducing energy consumption, the steps required in invoking energy savings configurations are often burdensome, inconvenient, or even difficult for users with minimal computer skills. Common solutions may also prohibit the continued processing or performing of a task in order to achieve the invoked energy savings; for example, though a user may select to reduce the amount of idle time required before his or her device automatically enters a lower-powered standby or idle mode, energy savings are only realized when the user stops actively engaging the device. Moreover, energy-savings methods often require the user to accept a corresponding reduction in computer processing speed or capabilities, and such performance reductions discourage users from selecting energy savings configurations. This may be particularly true when the costs of the energy used by faster/less efficient configurations are not born by the user or are not perceived by the user as large enough to justify reduced processing performance and/or increased times.
Thus, there is a need for improved methods and systems that address the above problems, as well as others.